The intent of this project is to develop new and improve existing instrumentation and to develop new experimental and data analysis methods for the characterization of biological macromolecules and the study of their interactions. In the area of the analysis of the data from analytical ultracentrifugation, further refinements have been made in the major breakthrough achieved by the use of light intensity data rather than light absorbency data from the analytical ultracentrifuge. The intensity data is advantageous because of its superior statistical properties and its potential to provide better parameter estimates in ultracentrifugal analyses. The major contribution in this area has been the development of a particularly robust and statistically valid method for estimating weights to be used when fitting light intensity data by non-linear least-squares curve-fitting. An invited paper describing this method of estimated weights is in preparation for publication in the prestigious series Methods in Enzymology. Experience with this methodology has revealed occasional sub-optimal results and also that it is still a rather time consuming and labor intensive method. Further statistical studies indicate that the error distribution of absorbance data, which is a logarithmically skewed Cauchy distribution, fall in the category of "fat-tailed" distributions, so called because of the relatively large distribution of deviations in the tails when compared to those in the central portion of the total distribution. Data with distributions of this type are better fit by L-l robust regression. This method utilizes minimization of the sum of the absolute values of the deviations of the data points with the reciprocal of the standard error of each point as its weight. It has the further virtue of being singularly insensitive to data "outliers," when compared to non-linear least-squares regression. Since absorbance data from the analytical ultracentrifuge is in the form of the absorbance and its standard error as a function of radial position, L-l fitting is singularly rapid and easy and has yielded such outstandingly superior results that it is now replacing other fitting techniques in this laboratory. It has proved to be equally useful for the fitting of thermodynamic data in the form of changes of free energy as a function of temperature, since there is usually no evidence that such data has a normal error distribution, which is the criterion for validity of non-linear least-squares regression. More experience has been gained with the new method of multi-wavelength analysis, which facilitates analysis of protein-protein and protein-nucleic acid interactions. In this method, one or both reactants are uniquely labeled with a chromophore; this permits a more definitive observation of the behavior, both alone and associated, of that component in the analysis by mathematical modeling. This new method has been utilized in Project 1 Z01 OD10039-06, "Physical Biochemistry of Macromolecules."